A Hierarchical Clustering Methodology for the Estimation of Toxicity

Photocatalytic degradation of antibiotic sulfamethizole by visible light activated perovskite LaZnO3

In this work, the perovskite LaZnO3 was synthesized via sol-gel method and applied for photocatalytic treatment of sulfamethizole (SMZ) antibiotics under visible light activation. SMZ was almost completely degraded (99.2% ± 0.3%) within 4 hr by photocatalyst LaZnO3 at the optimal dosage of 1.1 g/L, with a mineralization proportion of 58.7% ± 0.4%. The efficient performance of LaZnO3 can be attributed to its wide-range light absorption and the appropriate energy band edge levels, which facilitate the formation of active agents such as ·O2-, h+, and ·OH. The integration of RP-HPLC/Q-TOF-MS and DFT-based computational techniques revealed three degradation pathways of SMZ, which were initiated by the deamination reaction at the aniline ring, the breakdown of the sulfonamide moieties, and a process known as Smile-type rearrangement and SO2 intrusion. Corresponding toxicity of SMZ and the intermediates were analyzed by quantitative structure activity relationship (QSAR), indicating the effectiveness of LaZnO3-based photocatalysis in preventing secondary pollution of the intermediates to the ecosystem during the degradation process. The visible-light-activated photocatalyst LaZnO3 exhibited efficient performance in the occurrence of inorganic anions and maintained high durability across multiple recycling tests, making it a promising candidate for practical antibiotic treatment.

Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow

With the increased availability of chemical data in public databases, innovative techniques and algorithms have emerged for the analysis, exploration, visualization, and extraction of information from these data. One such technique is chemical grouping, where chemicals with common characteristics are categorized into distinct groups based on physicochemical properties, use, biological activity, or a combination. However, existing tools for chemical grouping often require specialized programming skills or the use of commercial software packages. To address these challenges, we developed a user-friendly chemical grouping workflow implemented in KNIME, a free, open-source, low/no-code, data analytics platform. The workflow serves as an all-encompassing tool, expertly incorporating a range of processes such as molecular descriptor calculation, feature selection, dimensionality reduction, hyperparameter search, and supervised and unsupervised machine learning methods, enabling effective chemical grouping and visualization of results. Furthermore, we implemented tools for interpretation, identifying key molecular descriptors for the chemical groups, and using natural language summaries to clarify the rationale behind these groupings. The workflow was designed to run seamlessly in both the KNIME local desktop version and KNIME Server WebPortal as a web application. It incorporates interactive interfaces and guides to assist users in a step-by-step manner. We demonstrate the utility of this workflow through a case study using an eye irritation and corrosion dataset.Scientific contributionsThis work presents a novel, comprehensive chemical grouping workflow in KNIME, enhancing accessibility by integrating a user-friendly graphical interface that eliminates the need for extensive programming skills. This workflow uniquely combines several features such as automated molecular descriptor calculation, feature selection, dimensionality reduction, and machine learning algorithms (both supervised and unsupervised), with hyperparameter optimization to refine chemical grouping accuracy. Moreover, we have introduced an innovative interpretative step and natural language summaries to elucidate the underlying reasons for chemical groupings, significantly advancing the usability of the tool and interpretability of the results.

Toxicity of Bromo-DragonFLY as a New Psychoactive Substance: Application of In Silico Methods for the Prediction of Key Toxicological Parameters Important to Clinical and Forensic Toxicology

Bromo-DragonFLY is a synthetic new psychoactive substance (NPS) that has gained attention due to its powerful and long-lasting hallucinogenic effects, legal status, and widespread availability. This study aimed to use various in silico toxicology methods to predict key toxicological parameters for Bromo-DragonFLY, including acute toxicity (LD50), genotoxicity, cardiotoxicity, health effects, and the potential for endocrine disruption. The results indicate significant acute toxicity with noticeable variations across different species, a low likelihood of genotoxic potential suggesting potential DNA damage, and a notable risk of cardiotoxicity associated with inhibition of the hERG channel. Evaluation of endocrine disruption suggests a low probability of Bromo-DragonFLY interacting with the estrogen receptor α (ER-α), indicating minimal estrogenic activity. These insights from in silico investigations are important for advancing our understanding of this NPS in forensic and clinical toxicology. These initial toxicological examinations establish a foundation for future research efforts and contribute to developing risk assessment and management strategies for using and misusing NPS.

Toxicity of New Psychoactive Substance (NPS): Threo-4-methylmethylphenidate (4-Mmph) - Prediction of toxicity using in silico methods

This study represents the first application of in silico methods to evaluate the toxicity of 4-methylphenidate (4-Mmph), a new psychoactive substance (NPS). Using advanced in silico toxicology tools, it was feasible to anticipate key aspects of 4-Mmph's toxicological profile, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and possible endocrine disruption. The findings indicate significant acute toxicity with variability among species, a high potential for adverse effects in the gastrointestinal system and lungs, a low genotoxic potential, a significant likelihood of skin irritation, and a notable cardiotoxicity risk associated with hERG channel inhibition. Evaluation of endocrine disruption revealed a low likelihood that 4-Mmph interacts with the estrogen receptor alpha (ER-α), indicating minimal estrogenic activity. These insights, derived from in silico studies, play a crucial role in improving the comprehension of 4-Mmph in forensic and clinical toxicology. These initial toxicological inquiries establish the foundation for future investigations and help formulate risk assessment and management strategies regarding the use and abuse of NPS. This article is part of a larger project funded by the Polish Ministry of Education and Science, titled "Toxicovigilance, Poisoning Prevention, and First Aid in Poisoning with Xenobiotics of Current Clinical Importance in Poland" (Grant Number SKN/SP/570184/2023).

Concentration levels and ecological risk assessment of typical organophosphate esters in representative surface waters of a megacity

Organophosphate esters (OPEs) have been widely used as flame retardants and plasticizers in consumer and industrial products. They have been found to have numerous exposure hazards. Recently, several OPEs have been detected in surface waters around the world, which may pose potential ecological risks to freshwater organisms. In this study, the concentration, spatial variation, and ecological risk of 15 OPEs in the Beiyun and Yongding rivers were unprecedentedly investigated by the ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and risk quotient (RQ) method. The result showed that triethyl phosphate (TEP), tri (2-chloroisopropyl) phosphate (TCPP) were the most abundant OPEs with average concentrations of 55.53 ng/L and 42.29 ng/L, respectively. The concentrations of OPEs in the Beiyun River are higher than in the Yongding River, and their levels were higher in densely populated and industrial areas. The risk assessment showed that there was insignificant from OPEs to freshwater organisms in these rivers (RQs <0.1). The risk was higher downstream than upstream, which was related to human-intensive industrial activities downstream in the Yongding River. The ecological risk of OPEs in surface waters worldwide was estimated by joint probability curves (JPCs), and the result showed that there was a moderate risk for tri (2-chloroethyl) phosphate (TCEP), a low risk for trimethyl phosphate (TMP), and insignificant for other OPEs. In addition, the QSAR-ICE-SSD model was used to calculate the hazardous concentration for 5% (HC5). This result validated the feasibility and accuracy of this model in predicting acute data of OPEs and reducing biological experiments on the toxicity of OPEs. These results revealed the ecological risk of OPEs and provided the scientific basis for environmental managers.

The estimation of acute oral toxicity (LD50) of G-series organophosphorus-based chemical warfare agents using quantitative and qualitative toxicology in silico methods

The idea of this study was the estimation of the theoretical acute toxicity (t-LD50, rat, oral dose) of organophosphorus-based chemical warfare agents from the G-series (n = 12) using different in silico methods. Initially identified in Germany, the G-type nerve agents include potent compounds such as tabun, sarin, and soman. Despite their historical significance, there is a noticeable gap in acute toxicity data for these agents. This study employs qualitative (STopTox and AdmetSAR) and quantitative (TEST; CATMoS; ProTox-II and QSAR Toolbox) in silico methods to predict LD50 values, offering an ethical alternative to animal testing. Additionally, we conducted quantitative extrapolation from animals, and the results of qualitative tests confirmed the acute toxicity potential of these substances and enabled the identification of toxicophoric groups. According to our estimations, the most lethal agents within this category were GV, soman (GD), sarin (GB), thiosarin (GBS), and chlorosarin (GC), with t-LD50 values (oral administration, extrapolated from rat to human) of 0.05 mg/kg bw, 0.08 mg/kg bw, 0.12 mg/kg bw, 0.15 mg/kg bw, and 0.17 mg/kg bw, respectively. On the contrary, compounds with a cycloalkane attached to the phospho-oxygen linkage, specifically methyl cyclosarin and cyclosarin, were found to be the least toxic, with values of 2.28 mg/kg bw and 3.03 mg/kg bw. The findings aim to fill the knowledge gap regarding the acute toxicity of these agents, highlighting the need for modern toxicological methods that align with ethical considerations, next-generation risk assessment (NGRA) and the 3Rs (replacement, reduction and refinement) principles.

Toxicity of the New Psychoactive Substance (NPS) Clephedrone (4-Chloromethcathinone, 4-CMC): Prediction of Toxicity Using In Silico Methods for Clinical and Forensic Purposes

This study reports the first application of in silico methods to assess the toxicity of 4-chloromethcathinone (4-CMC), a novel psychoactive substance (NPS). Employing advanced toxicology in silico tools, it was possible to predict crucial aspects of the toxicological profile of 4-CMC, including acute toxicity (LD50), genotoxicity, cardiotoxicity, and its potential for endocrine disruption. The obtained results indicate significant acute toxicity with species-specific variability, moderate genotoxic potential suggesting the risk of DNA damage, and a notable cardiotoxicity risk associated with hERG channel inhibition. Endocrine disruption assessment revealed a low probability of 4-CMC interacting with estrogen receptor alpha (ER-α), suggesting minimal estrogenic activity. These insights, derived from in silico studies, are critical in advancing the understanding of 4-CMC properties in forensic and clinical toxicology. These initial toxicological findings provide a foundation for future research and aid in the formulation of risk assessment and management strategies in the context of the use and abuse of NPSs.

Addressing Cardiovascular Toxicity Risk of Electronic Nicotine Delivery Systems in the Twenty-First Century: "What Are the Tools Needed for the Job?" and "Do We Have Them?"

Cigarette smoking is positively and robustly associated with cardiovascular disease (CVD), including hypertension, atherosclerosis, cardiac arrhythmias, stroke, thromboembolism, myocardial infarctions, and heart failure. However, after more than a decade of ENDS presence in the U.S. marketplace, uncertainty persists regarding the long-term health consequences of ENDS use for CVD. New approach methods (NAMs) in the field of toxicology are being developed to enhance rapid prediction of human health hazards. Recent technical advances can now consider impact of biological factors such as sex and race/ethnicity, permitting application of NAMs findings to health equity and environmental justice issues. This has been the case for hazard assessments of drugs and environmental chemicals in areas such as cardiovascular, respiratory, and developmental toxicity. Despite these advances, a shortage of widely accepted methodologies to predict the impact of ENDS use on human health slows the application of regulatory oversight and the protection of public health. Minimizing the time between the emergence of risk (e.g., ENDS use) and the administration of well-founded regulatory policy requires thoughtful consideration of the currently available sources of data, their applicability to the prediction of health outcomes, and whether these available data streams are enough to support an actionable decision. This challenge forms the basis of this white paper on how best to reveal potential toxicities of ENDS use in the human cardiovascular system-a primary target of conventional tobacco smoking. We identify current approaches used to evaluate the impacts of tobacco on cardiovascular health, in particular emerging techniques that replace, reduce, and refine slower and more costly animal models with NAMs platforms that can be applied to tobacco regulatory science. The limitations of these emerging platforms are addressed, and systems biology approaches to close the knowledge gap between traditional models and NAMs are proposed. It is hoped that these suggestions and their adoption within the greater scientific community will result in fresh data streams that will support and enhance the scientific evaluation and subsequent decision-making of tobacco regulatory agencies worldwide.

The acute toxicity of Novichok's degradation products using quantitative and qualitative toxicology in silico methods

The emergence of Novichok agents, potent organophosphorus nerve agents, has spurred the demand for advanced analytical methods and toxicity assessments as a result of their involvement in high-profile incidents. This study focuses on the degradation products of Novichok agents, particularly their potential toxic effects on biological systems. Traditional in vivo methods for toxicity evaluation face ethical and practical constraints, prompting a shift toward in silico toxicology research. In this context, we conducted a comprehensive qualitative and quantitative analysis of acute oral toxicity (AOT) for Novichok degradation products, using various in silico methods, including TEST, CATMoS, ProTox-II, ADMETlab, ACD/Labs Percepta, and QSAR Toolbox. Adopting these methodologies aligns with the 3Rs principle, emphasising Replacement, Reduction, and Refinement in the realm of toxicological studies. Qualitative assessments with STopTox and admetSAR revealed toxic profiles for all degradation products, with predicted toxicophores highlighting structural features responsible for toxicity. Quantitative predictions yielded varied estimates of acute oral toxicity, with the most toxic degradation products being EOPAA, MOPGA, MOPAA, MPGA, EOPGA, and MPAA, respectively. Structural modifications common to all examined hydrolytic degradation products involve substituting the fluorine atom with a hydroxyl group, imparting consequential effects on toxicity. The need for sophisticated analytical techniques for identifying and quantifying Novichok degradation products is underscored due to their inherent reactivity. This study represents a crucial step in unravelling the complexities of Novichok toxicity, highlighting the ongoing need for research into its degradation processes to refine analytical methodologies and fortify readiness against potential threats.

Theory-guided unraveling of the mechanism underlying Cu1.0/Mn1.0-ZnO with dual reaction centers for enhanced peroxymonosulfate activation

Developing efficient catalytic systems for water contamination removal is a topic of great interest. However, the use of heterogeneous catalysts faces challenges due to insufficient active sites and electron cycling. In this study, results from first-principles calculations demonstrate that dual reaction centers (DRCs) are produced around the Cu and Mn sites in Cu1.0/Mn1.0-ZnO due to the electronegativity difference. Experimental results reveal the material with DRCs greatly enhances electron transfer efficiency and significantly impacts the oxidation and reduction of peroxymonosulfate (PMS). In addition, the self-consistent potential correction (SCPC) method was introduced to correct the energy and charge of charged periodic systems simulating a catalytic process, resulting in more precise catalytic results. Specifically, the material exhibits a preference for adsorbing negatively charged PMS anions at electron-deficient Mn sites, facilitating PMS oxidation for the generation of 1O2, and PMS reduction around the electron-rich Cu for the formation of •OH and SO4•-. The major reactive oxygen species is 1O2, showcasing effective performance in various degradation systems. Overall, our work provides novel insights into the persulfate-based heterogeneous catalytic oxidation process, paving the way for the development of high-performance catalytic systems for water purification.

Prediction of HC5s for phthalate esters by use of the QSAR-ICE model and ecological risk assessment in Chinese surface waters

Due to their endocrine-disrupting effects and the risks posed in surface waters, in particular by chronic low-dose exposure to aquatic organisms, phthalate esters (PAEs) have received significant attention. However, most assessments of risks posed by PAEs were performed at a selection level, and thus limited by empirical data on toxic effects and potencies. A quantitative structure activity relationship (QSAR) and interspecies correlation estimation (ICE) model was constructed to estimate hazardous concentrations (HCs) of selected PAEs to aquatic organisms, then they were used to conduct a multiple-level environmental risk assessment for PAEs in surface waters of China. Values of hazardous concentration for 5% of species (HC5s), based on acute lethality, estimated by use of the QSAR-ICE model were within 1.25-fold of HC5 values derived from empirical data on toxic potency, indicating that the QSAR-ICE model predicts the toxicity of these three PAEs with sufficient accuracy. The five selected PAEs may be commonly measured in China surface waters at concentrations between ng/L and μg/L. Risk quotients according to median concentrations of the five PAEs ranged from 3.24 for di(2-ethylhexhyl) phthalate (DEHP) to 4.10 × 10-3 for dimethyl phthalate (DMP). DEHP and dibutyl phthalate (DBP) had risks to the most vulnerable aquatic biota, with the frequency of exceedances of the predicted no-effect concentration (PNECs) of 75.5% and 38.0%, respectively. DEHP and DBP were identified as having "high" or "moderate" risks. Results of the joint probability curves (JPC) method indicated DEHP posed "intermediate" risk to freshwater species with a maximum risk product of 5.98%. The multiple level system introduced in this study can be used to prioritize chemicals and other new pollutant in the aquatic ecological.

Side effects of the addition of an adsorbent for the nitrification performance of a microbiome in the treatment of an antibiotic mixture

This work determined the effect of biochar (BC) as an adsorbent on the nitrifying microbiome in regulating the removal, transformation, fate, toxicity, and potential environmental consequences of an antibiotic mixture containing oxytetracycline (OTC) and sulfamethoxazole (SMX). Despite the beneficial role of BC as reported in the literature, the present study revealed side effects for the nitrifying microbiome and its functioning arising from the presence of BC. Long-term monitoring revealed severe disruption to nitratation via the inhibition of both nitrite oxidizers (e.g., Nitrospira defluvii) and potential comammox species (e.g., Ca. Nitrospira nitrificans). Byproducts (BPs) more toxic than the parent compounds were found to persist at a high relative abundance, particularly in the presence of BC. Quantitative structure-activity relationship modeling determined that the physicochemical properties of the toxic BPs significantly differed from those of OTC and SMX. The results suggested that the BPs tended to mobilize and accumulate on the surface of the solids in the system (i.e., the BC and biofilm), disrupting the nitrifiers growing at the interface. Collectively, this study provides novel insights, demonstrating that the addition of adsorbents to biological systems may not necessarily be beneficial; rather, they may generate side effects for specific bacteria that have important ecosystem functions.

Environmental consequences of transformation products from an antibiotic mixture and their mitigation in a wastewater microbiome using an HCl-modified adsorbent

This study enhanced our understanding of antibiotic mixtures' occurrence, transformation, toxicity, and ecological risks. The role of acid-modified biochar (BC) in treating antibiotic residues was explored, shedding light on how BC influences the fate, mobility, and environmental impact of antibiotics and transformation products (TPs) in an activated sludge (AS) microbiome. A mixture of oxytetracycline and sulfamethoxazole was found to synergistically (or additively) inhibit cell growth of AS and disrupt the microbiome structure, species richness/diversity, and function. The formation of TPs with potentially higher toxicity and persistence than the original compounds was identified, explaining the microbiome disruption. Agricultural waste-derived BC was optimized for contaminant adsorption, leading to a reduction in toxicity when added to AS by sequestering TPs on its surface. This work highlighted adsorbents as a practical engineering strategy for mitigating liquid-phase contaminants' toxicological consequences, proactively controlling the fate and effects of antibiotics and TPs.

Solar photocatalysis application in UWWTP outlets - simulations based on predictive models in flat-plate reactors and pollutant degradation studies with in silico toxicity assessment

The application of the solar photocatalysis for the degradation of residual pollutants found in surface water was demonstrated. Semi-pilot scale flat-plate cascade reactor (FPCR) was used to study the degradation of model organic pollutants: enrofloxacin (ENRO), 17β-estradiol (E2) and 1H-benzotriazole (1H-BT) over TiO2 thin-film supported on glass fibers. A modular panel with full-spectra solar lamps with appropriate UVB and UVA irradiation levels was used as a simulation of sunlight. Pollutant degradation in FPCR was estimated using predictive models; intrinsic reaction rate constants (ki) for ENRO, E2 and 1H-BT independent of the reactor size, flow rate and irradiation conditions were determined: 9.60, 3.35 and 0.37 109 s-1 W-0.5 m1.5, respectively. Main degradation products (DPs), formed upon hydroxylation, ring opening and oxidation, were identified using LC-QTOF-MS. The ecotoxicological impact was assessed via T.E.S.T. and ECOSAR open-source tools showing the formation of less harmful DPs after sufficient reaction time. Pollutant degradation was simulated at four locations of interest, i.e. exhausts from urban wastewater treatment plants (UWWTPs) in Zagreb, Croatia (45°N), Krakow, Poland (50°N), Sevilla, Spain (37°N) and Ioannina, Greece (39.6°N). Results have proved that a simple flat-plate system with supported photocatalysts can be easily scaled up and incorporated at the outlet of UWWTP for the reduction of pollutant load and related toxicity. The exhaust canal in Zagreb with the estimated length of a photocatalytic layer of 122 m for the > 90% degradation of all target pollutants was discussed as the best installation site among studied locations. ENVIRONMENTAL IMPLICATION: A multi-disciplinary approach to the tentative application of TiO2 solar photocatalysis outdoors to reduce pollutant loads and toxicity in surface waters was demonstrated. Possible application at four selected locations in Europe, as an additional step in water treatment after urban wastewater treatment plants (UWWTPs) was discussed. Target pollutants were studied under environmentally relevant conditions (sunlight levels, water matrix, simulation of process on a real scale at selected geographical location), at both higher and low concentrations.

Predicted no-effect concentration for eight PAHs and their ecological risks in seven major river systems of China

The initial step in the assessment of the ecological risk of pollutants is to determine the predicted no-effect concentration (PNEC). However, ecological risk assessments of eight carcinogenic polycyclic aromatic hydrocarbons (PAHs), including dimethylbenz[a]anthracene (DMBA), methylcholanthrene (MCA), benzo(a)anthracene (BaA), chrysene (CHR), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), and dibenzo(a,h)anthracene (DBA), are rarely conducted due to the lack of their PNECs based on test data. In this study, quantitative structure-activity relationship (QSAR) models and interspecies correlation estimation (ICE) models were combined to predict the acute toxicity of these eight target PHAs. A Kolmogorov-Smirnov analysis for species sensitivity distributions (SSDs) of native and all species was conducted. There was no significant difference between the predictions for native Chinese species and the predictions for all species by the QSAR-ICE models. In addition, the feasibility of the QSAR-ICE models was demonstrated by comparing the SSD curves constructed by measured toxicity data of BaP and those predicted by the QSAR-ICE models. The PNECs of the eight PAHs were estimated based on the SSDs and acute to chronic ratio (ACR) method; these data were 0.071 μg/L, 0.033 μg/L, 0.049 μg/L, 0.114 μg/L, 0.019 μg/L, 0.021 μg/L, 0.038 μg/L and 0.054 μg/L for DMBA, DBA, BaP, MCA, BaA, CHR, BbF, BkF, respectively. The higher PNECs of the alkylated PAHs suggested their lower ecological risks. Based on the mixed risk quotient (mRQ) of PAHs through the concentration addition (CA) model, high ecological risk watersheds, such as the Songhua River (mRQ = 1.95), the Liao River (mRQ = 4.59), and the Huai River (mRQ = 1.93), were identified.

The prediction of acute toxicity (LD50) for organophosphorus-based chemical warfare agents (V-series) using toxicology in silico methods

Nerve agents are organophosphate chemical warfare agents that exert their toxic effects by irreversibly inhibiting acetylcholinesterase, affecting the breakdown of the neurotransmitter acetylcholine in the synaptic cleft. Due to the risk of exposure to dangerous nerve agents and for animal welfare reasons, in silico methods have been used to assess acute toxicity safely. The next-generation risk assessment (NGRA) is a new approach for predicting toxicological parameters that can meet modern requirements for toxicological research. The present study explains the acute toxicity of the examined V-series nerve agents (n = 9) using QSAR models. Toxicity Estimation Software Tool (ver. 4.2.1 and ver. 5.1.2), QSAR Toolbox (ver. 4.6), and ProTox-II browser application were used to predict the median lethal dose. The Simplified Molecular Input Line Entry Specification (SMILES) was the input data source. The results indicate that the most deadly V-agents were VX and VM, followed by structural VX analogues: RVX and CVX. The least toxic turned out to be V-sub x and Substance 100A. In silico methods for predicting various parameters are crucial for filling data gaps ahead of experimental research and preparing for the upcoming use of nerve agents.

An integrated assessment of ecological and human health risks of per- and polyfluoroalkyl substances through toxicity prediction approaches

Per- and polyfluoroalkyl substances (PFAS) are also known as "forever chemicals" due to their persistence and ubiquitous environmental distribution. This review aims to summarize the global PFAS distribution in surface water and identify its ecological and human risks through integrated assessment. Moreover, it provides a holistic insight into the studies highlighting the human biomonitoring and toxicological screening of PFAS in freshwater and marine species using quantitative structure-activity relationship (QSAR) based models. Literature showed that PFOA and PFOS were the most prevalent chemicals found in surface water. The highest PFAS levels were reported in the US, China, and Australia. The TEST model showed relatively low LC50 of PFDA and PFOS for Pimephales promelas (0.36 and 0.91 mg/L) and high bioaccumulation factors (518 and 921), revealing an elevated associated toxicity. The risk quotients (RQs) values for P. promelas and Daphnia magna were found to be 269 and 23.7 for PFOS. Studies confirmed that long-chain PFAS such as PFOS and PFOA undergo bioaccumulation in aquatic organisms and induce toxicological effects such as oxidative stress, transgenerational epigenetic effects, disturbed genetic and enzymatic responses, perturbed immune system, hepatotoxicity, neurobehavioral toxicity, altered genetic and enzymatic responses, and metabolism abnormalities. Human biomonitoring studies found the highest PFOS, PFOA, and PFHxS levels in urine, cerebrospinal fluid, and serum samples. Further, long-chain PFOA and PFOS exposure create severe health implications such as hyperuricemia, reduced birth weight, and immunotoxicity in humans. Molecular docking analysis revealed that short-chain PFBS (-11.84 Kcal/mol) and long-chain PFUnDA (-10.53 Kcal/mol) displayed the strongest binding interactions with human serum albumin protein. Lastly, research challenges and future perspectives for PFAS toxicological implications were also discussed, which helps to mitigate associated pollution and ecological risks.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

Application of toxicology in silico methods for prediction of acute toxicity (LD50) for Novichoks

Novichoks represent the fourth generation of chemical warfare agents with paralytic and convulsive effects, produced clandestinely during the Cold War by the Soviet Union. This novel class of organophosphate compounds is characterised by severe toxicity, which, for example, we have already experienced three times (Salisbury, Amesbury, and Navalny's case) as a society. Then the public debate about the true nature of Novichoks began, realising the importance of examining the properties, especially the toxicological aspects of these compounds. The updated Chemical Warfare Agents list registers over 10,000 compounds as candidate structures for Novichoks. Consequently, conducting experimental research for each of them would be a huge challenge. Additionally, due to the enormous risk of contact with hazardous Novichoks, in silico assessments were applied to estimate their toxicity safely. In silico toxicology provides a means of identifying hazards of compounds before synthesis, helping to fill gaps and guide risk minimisation strategies. A new approach to toxicology testing first considers the prediction of toxicological parameters, eliminating unnecessary animal studies. This new generation risk assessment (NGRA) can meet the modern requirements of toxicological research. The present study explains, using QSAR models, the acute toxicity of the Novichoks studied (n = 17). The results indicate that the toxicity of Novichoks varies. The deadliest turned out to be A-232, followed by A-230 and A-234. On the other hand, the "Iranian" Novichok and C01-A038 compounds turned out to be the least toxic. Developing reliable in silico methods to predict various parameters is essential to prepare for the upcoming use of Novichoks.

Transparency in Modeling through Careful Application of OECD's QSAR/QSPR Principles via a Curated Water Solubility Data Set

The need for careful assembly, training, and validation of quantitative structure-activity/property models (QSAR/QSPR) is more significant than ever as data sets become larger and sophisticated machine learning tools become increasingly ubiquitous and accessible to the scientific community. Regulatory agencies such as the United States Environmental Protection Agency must carefully scrutinize each aspect of a resulting QSAR/QSPR model to determine its potential use in environmental exposure and hazard assessment. Herein, we revisit the goals of the Organisation for Economic Cooperation and Development (OECD) in our application and discuss the validation principles for structure-activity models. We apply these principles to a model for predicting water solubility of organic compounds derived using random forest regression, a common machine learning approach in the QSA/PR literature. Using public sources, we carefully assembled and curated a data set consisting of 10,200 unique chemical structures with associated water solubility measurements. This data set was then used as a focal narrative to methodically consider the OECD's QSA/PR principles and how they can be applied to random forests. Despite some expert, mechanistically informed supervision of descriptor selection to enhance model interpretability, we achieved a model of water solubility with comparable performance to previously published models (5-fold cross validated performance 0.81 R2 and 0.98 RMSE). We hope this work will catalyze a necessary conversation around the importance of cautiously modernizing and explicitly leveraging OECD principles while pursuing state-of-the-art machine learning approaches to derive QSA/PR models suitable for regulatory consideration.

Predicted health and environmental hazards of liquid crystal materials via quantitative structure-property relationship modeling

Liquid crystal materials (LCMs) are considered as emerging contaminants with high persistent and bioaccumulative potentials, but their toxicological effects are not well understood. To address this issue, a list of 1431 LCMs commercially available in the market was established through literature reviews and surveys of LCM suppliers. Toxicological properties of 221 target LCMs were derived from the Classification and Labeling Inventory by the European Chemicals Agency. More than 80 % of target LCMs likely pose adverse effects on human health or aquatic ecosystems. Two quantitative structure-property relationship (QSPR) models developed from the toxicological properties of LCMs achieved approximately 90 % accuracy in external data sets. The probability-based approach was more efficient in defining the applicability domain for the QSPR models than a range- or distance-based approach. The highest accuracy was achieved for chemicals within the probability-based applicability domain. The QSPR models were applied to predict health and environmental hazards of 1210 LCMs that had not been notified to the Classification and Labeling Inventory, and 301 and 94 LCMs were recognized as posing potential hazards to human health and the environment, respectively. The present study highlights the potential detrimental effects of LCMs and offers a specific in silico technique for screening hazardous LCMs.

Identification of novel human nicotinamide N-methyltransferase inhibitors: a structure-based pharmacophore modeling and molecular dynamics approach

Human nicotinamide N-methyltransferase (hNNMT) is a cytosolic enzyme associated in the phase-II metabolism, belonging to the S-adenosyl-L-methionine (SAM)-dependent methyltransferases family. Overexpression of hNNMT was observed in diseases such as metabolic disorders and different types of cancers, which suggest NNMT as a prospective therapeutic target. In this study we propose a structure-based pharmacophore model to understand the structural features responsible for the pharmacological activity. The generated model was validated using the ROC curve (AUC), goodness of hit score (GH), specificity, sensitivity and enrichment factor (EF). The pharmacophore was employed to retrieve active molecules from the ZINC database, followed by virtual-screening and molecular docking. Six molecules with the best pharmfit score, binding energy and ADMET properties were identified in this study. A 150 ns molecular dynamics simulation was performed on the selected molecules complexed with hNNMT protein to validate the results. The molecules ZINC35464499, ZINC13311192, ZINC31159282, ZINC14650833, ZINC14819515 and ZINC00303881 were identified, which could be act as the potential hNNMT inhibitors and can also be used as direct hits for developing novel hNNMT antagonists.Communicated by Ramaswamy H. Sarma.

Cytotoxic evaluation of YSL-109 in a triple negative breast cancer cell line and toxicological evaluations

Breast cancer (BC) is the leading cause of cancer-related death in women worldwide. Triple negative breast cancer (TNBC) is the most aggressive form of BC being with the worst prognosis and the worst survival rates. There is no specific pharmacological target for the treatment of TNBC; conventional therapy includes the use of non-specific chemotherapy that generally has a poor prognosis. Therefore, the search of effective therapies against to TNBC continues at both preclinical and clinical level. In this sense, the exploration of different pharmacological targets is a continue task that pave the way to epigenetic modulation using novel small molecules. Lately, the inhibition of histone deacetylases (HDACs) has been explored to treat different BC, including TNBC. HDACs remove the acetyl groups from the ɛ-amino lysine resides on histone and non-histone proteins. In particular, the inhibition of HDAC6 has been suggested to be useful for the treatment of TNBC due to it is overexpressed in TNBC. Therefore, in this work, an HDAC6 selective inhibitor, the (S)-4-butyl-N-(1-(hydroxyamino)-3-(naphthalen-1-yl)-1-oxopropan-2-yl) benzamide (YSL-109), was assayed on TNBC cell line (MDA-MB231) showing an antiproliferative activity (IC₅₀ = 50.34 ± 1.11 µM), whereas on fibroblast, it was lesser toxic. After corroborating the in vitro antiproliferative activity of YSL-109 in TNBC, the toxicological profile was explored using combined approach with in silico tools and experimental assays. YSL-109 shows moderate mutagenic activity on TA-98 strain at 30 and 100 µM in the Ames test, whereas YSL-109 did not show in vivo genotoxicity and its oral acute toxicity (LD₅₀) in CD-1 female mice was higher than 2000 mg/kg, which is in agreement with our in silico predictions. According to these results, YSL-109 represents an interesting compound to be explored for the treatment of TNBC under preclinical in vivo models.

Therapeutic potential of compounds targeting SARS-CoV-2 helicase

The economical and societal impact of COVID-19 has made the development of vaccines and drugs to combat SARS-CoV-2 infection a priority. While the SARS-CoV-2 spike protein has been widely explored as a drug target, the SARS-CoV-2 helicase (nsp13) does not have any approved medication. The helicase shares 99.8% similarity with its SARS-CoV-1 homolog and was shown to be essential for viral replication. This review summarizes and builds on existing research on inhibitors of SARS-CoV-1 and SARS-CoV-2 helicases. Our analysis on the toxicity and specificity of these compounds, set the road going forward for the repurposing of existing drugs and the development of new SARS-CoV-2 helicase inhibitors.

In-silico molecular interactions among the secondary metabolites of Caulerpa spp. and colorectal cancer targets

Caulerpa spp. secrete more than thirty different bioactive chemicals which have already been used in cancer treatment research since they play a pivotal role in cancer metabolism. Colorectal cancer is one of the most common cancer types, thus using novel and effective chemicals for colorectal cancer treatment is crucial. In the cheminformatics pipeline of this study, ADME-Tox and drug-likeness tests were performed for filtering the secondary metabolites of Caulerpa spp. The ligands which were selected from the ADME test were used for in silico molecular docking studies against the enzymes of the oxidative branch of the pentose phosphate pathway (glucose-6-phosphate dehydrogenase and 6-phosphoglutarate dehydrogenase), which is of great importance for colorectal cancer, by using AutoDock Vina. Pharmacophore modeling was carried out to align the molecules. Molecular dynamic simulations were performed for each target to validate the molecular docking studies and binding free energies were calculated. According to the ADME test results, 13 different secondary metabolites were selected as potential ligands. Molecular docking studies revealed that vina scores of caulerpin and monomethyl caulerpinate for G6PDH were found as -10.6 kcal mol-1, -10.5 kcal mol-1, respectively. Also, the vina score of caulersin for 6PGD was found as -10.7 kcal mol-1. The highest and the lowest binding free energies were calculated for monomethyl caulerpinate and caulersin, respectively. This in silico study showed that caulerpin, monomethyl caulerpinate, and caulersin could be evaluated as promising marine phytochemicals against pentose phosphate pathway enzymes and further studies are recommended to investigate the detailed activity of these secondary metabolites on these targets.

Relationships Between Aquatic Toxicity, Chemical Hydrophobicity, and Mode of Action: Log Kow Revisited

Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.

A QSAR-ICE-SSD model prediction of the PNECs for alkylphenol substances and application in ecological risk assessment for rivers of a megacity

Alkylphenols (APs) are ubiquitous and generally present in higher residue levels in the environment. The present work focuses on the development of a set of in silico models to predict the aquatic toxicity of APs with incomplete/unknown toxicity data in aquatic environments. To achieve this, a QSAR-ICE-SSD model was constructed for aquatic organisms by combining quantitative structure-activity relationship (QSAR), interspecies correlation estimation (ICE), and species sensitivity distribution (SSD) models in order to obtain the hazardous concentrations (HCs) of selected APs. The research indicated that the keywords "alkylphenol" and "nonylphenol" were most commonly studied. The selected ICE models were robust (R2: 0.70-0.99; p-value < 0.01). All models had a high reliability cross- validation success rates (>75%), and the HC5 predicted with the QSAR-ICE-SSD model was 2-fold than that derived with measured experimental data. The HC5 values demonstrated nearly linear decreasing trend from 2-MP to 4-HTP, while the decreasing trend from 4-HTP to 4-DP became shallower, indicates that the toxicity of APs to aquatic organisms increases with the addition of alkyl carbon chain lengths. The ecological risks assessment (ERA) of APs revealed that aquatic organisms were at risk from exposure to 4-NP at most river stations (the highest risk quotient (RQ) = 1.51), with the highest relative risk associated with 2.9% of 4-NP detected in 82.9% of the sampling sites. The targeted APs posed potential ecological risks in the Yongding and Beiyun River according to the mixture ERA. The potential application of QSAR-ICE-SSD models could satisfy the immediate needs for HC5 derivations without the need for additional in vivo testing.

Emissions of Liquid Crystal Monomers from Obsolete Smartphone Screens in Indoor Settings: Characteristics and Human Exposure Risk

Liquid crystal monomers (LCMs) have been found to accumulate in indoor environments, but the emission kinetics of LCMs from electronic devices are not well understood. Leakage from damaged liquid crystal displays may be an important mechanism for LCMs to enter the environment and become potential health hazards to humans. To address this issue, we conducted chamber experiments to characterize the emissions of LCMs from obsolete smartphone screens and estimated the doses of residential and occupational exposures to LCMs. The emission rates of the detected LCMs were in the ranges of 0.1-7 μg m^-2 h^-1 at 80 °C, 0.05-7 μg m^-2 h^-1 at 60 °C, and 0.002-0.2 μg m^-2 h^-1 at 25 °C. Liquid crystal monomers with large molecular weights and low volatilities tended to accumulate at screen surfaces and were re-emitted at elevated temperatures, leading to high emission rates of heavy LCMs upon thermal treatment. The estimated doses of residential and occupational exposures to individual LCMs were 0.0001-0.009 and 0.007-2 ng kg^-1 d^-1, respectively. As LCMs are potentially carcinogenic based on in silico assessments, LCMs emitted from obsolete smartphones in indoor settings may become human health hazards.

Targeting SARS-CoV-2 non-structural protein 13 via helicase-inhibitor-repurposing and non-structural protein 16 through pharmacophore-based screening

Novel drug compound hunting was carried out for SARS-CoV-2 proteins with low mutation susceptibility. The probability of escape mutation and drug resistance is lower if conserved microbial proteins are targeted by therapeutic drugs. Mutation rate of all SARS-CoV-2 proteins were analyzed via multiple sequence alignment Non-Structural Protein 13 and Non-Structural Protein 16 were selected for the current study due to low mutation rate among viral strains and significant functionality. Cross-species mutation rate analysis for NSP13 and NSP16 showed these are well-conserved proteins among four coronaviral species. Viral helicase inhibitors, identified using literature-mining, were docked against NSP13. Pharmacophore-based screening of 11,375 natural compounds was conducted for NSP16. Stabilities of top compounds inside human body were confirmed via molecular dynamic simulation. ADME properties and LD₅₀ values of the helicase inhibitors and Ambinter natural compounds were analyzed. Compounds against NSP13 showed binding affinities between −10 and −5.9 kcal/mol whereby ivermectin and scutellarein showed highest binding energies of −10 and −9.9 kcal/mol. Docking of 18 hit compounds against NSP16 yielded binding affinities between −8.9 and −4.1 kcal/mol. Hamamelitannin and deacyltunicamycin were the top compounds with binding affinities of −8.9 kcal/mol and −8.4 kcal/mol. The top compounds showed stable ligand–protein interactions in molecular dynamics simulation. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, high binding affinity and molecular interactions. Conversion of these compounds into drugs after in vitro experimentation can become better treatment options to elevate COVID management.

Assessing the External Exposome Using Wearable Passive Samplers and High-Resolution Mass Spectrometry among South African Children Participating in the VHEMBE Study

Children in low- and middle-income countries are often exposed to higher levels of chemicals and are more vulnerable to the health effects of air pollution. Little is known about the diversity, toxicity, and dynamics of airborne chemical exposures at the molecular level. We developed a workflow employing state-of-the-art wearable passive sampling technology coupled with high-resolution mass spectrometry to comprehensively measure 147 children's personal exposures to airborne chemicals in Limpopo, South Africa, as part of the Venda Health Examination of Mothers, Babies, and Their Environment (VHEMBE). 637 environmental exposures were detected, many of which have never been measured in this population; of these 50 airborne chemical exposures of concern were detected, including pesticides, plasticizers, organophosphates, dyes, combustion products, and perfumes. Biocides detected in wristbands included p,p'-dichlorodiphenyltrichloroethane (p,p'-DDT), p,p'-dichlorodiphenyldichloroethane (p,p'-DDD), p,p'-dichlorodiphenyldichloroethylene (p,p'-DDE), propoxur, piperonyl butoxide, and triclosan. Exposures differed across the assessment period with 27% of detected chemicals observed to be either higher or lower in the wet or dry seasons.

The use of Bayesian methodology in the development and validation of a tiered assessment approach towards prediction of rat acute oral toxicity

There exists consensus that the traditional means by which safety of chemicals is assessed—namely through reliance upon apical outcomes obtained following in vivo testing—is increasingly unfit for purpose. Whilst efforts in development of suitable alternatives continue, few have achieved levels of robustness required for regulatory acceptance. An array of “new approach methodologies” (NAM) for determining toxic effect, spanning in vitro and in silico spheres, have by now emerged. It has been suggested, intuitively, that combining data obtained from across these sources might serve to enhance overall confidence in derived judgment. This concept may be formalised in the “tiered assessment” approach, whereby evidence gathered through a sequential NAM testing strategy is exploited so to infer the properties of a compound of interest. Our intention has been to provide an illustration of how such a scheme might be developed and applied within a practical setting—adopting for this purpose the endpoint of rat acute oral lethality. Bayesian statistical inference is drawn upon to enable quantification of degree of confidence that a substance might ultimately belong to one of five LD50-associated toxicity categories. Informing this is evidence acquired both from existing in silico and in vitro resources, alongside a purposely-constructed random forest model and structural alert set. Results indicate that the combination of in silico methodologies provides moderately conservative estimations of hazard, conducive for application in safety assessment, and for which levels of certainty are defined. Accordingly, scope for potential extension of approach to further toxicological endpoints is demonstrated.

Detection and toxicity modeling of anthraquinone dyes and chlorinated side products from a colored smoke pyrotechnic reaction

"Green" pyrotechnics seek to remove known environmental pollutants and health hazards from their formulations. This chemical engineering approach often focuses on maintaining performance effects upon replacement of objectionable ingredients, yet neglects the chemical products formed by the exothermic reaction. In this work, milligram quantities of a lab-scale pyrotechnic red smoke composition were functioned within a thermal probe for product identification by pyrolysis-gas chromatography-mass spectrometry. Thermally decomposed ingredients and new side product derivatives were identified at lower relative abundances to the intact organic dye (as the engineered sublimation product). Side products included chlorination of the organic dye donated by the chlorate oxidizer. Machine learning quantitative structure-activity relationship models computed impacts to health and environmental hazards. High to very high toxicities were predicted for inhalation, mutagenicity, developmental, and endocrine disruption for common military pyrotechnic dyes and their analogous chlorinated side products. These results underscore the need to revise objectives of "green" pyrotechnic engineering.

Nontargeted identification of chlorinated disinfection byproducts formed from natural organic matter using Orbitrap mass spectrometry and a halogen extraction code

Natural organic matter is a major source of precursors of hazardous chlorinated disinfection byproducts (Cl-DBPs) formed during water treatment, but the majority of Cl-DBPs are still unidentified. In this study, we used a self-written halogen extraction code to identify halogen isotopic patterns in combination with the R package MFAssignR, to identify Cl-DBPs from Orbitrap mass spectra. One hundred and eighty-nine Cl-DBPs were detected during chlorination of a Suwannee River natural organic matter solution, and the structures of 20 of these compounds are reported for the first time. Kendrick mass defect analysis and structural identification confirmed that chlorinated carboxylic acids are common and likely to form during chlorination. A toxicity prediction using quantitative structure-activity relationship models indicated that most of the chlorinated carboxylic acids may be highly toxic. Our analytical strategy can identify Cl-DBPs accurately from complex mixtures and may also be applicable to the identification of other halogenated disinfection byproducts formed during water treatment.

Natural dye from Caesalpinia sappan L. heartwood for eco-friendly coloring of recycled paper based packing material and its in silico toxicity analysis

The uses of natural dyes are getting popularized due to the increased awareness regarding the toxicity of many chemical colorants. The chemical colorants are being replaced by the natural colorants for the various industrial applications. The plant-based natural colorants are considered eco-friendly and toxic free. In the present study, we report a natural dye from the heartwood of Caesalpinia sappan suitable for paper based packing materials. This forms the first report on the study of natural dye obtained from the heartwood of C. sappan on paper material. The extracted dye had a good photostability and able to make imprints on recycled paper bags. Moreover, a significant inhibition of bacterial growth was observed at a higher dye concentration of 100 μg mL^-1 against P. aeruginosa which was higher than the standard antibiotics. Growth inhibition was also observed in case of B. subtilis (22 ± 0.17 mm) and K. pneumonia (21 ± 0.53 mm) at 100 μg mL^-1. The dye could be used in making medicated packing materials and have many other bio-potential which was validated through in silico toxicity analysis. The application of such natural dyes in paper material value addition will help in a cleaner and sustainable process during paper recycling.

AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex biomolecular data sets. The AweGNN is a neural network model based on geometric-graph features of element-pair interactions, with its graph parameters being updated throughout the training, which results in what we call a network-enabled automatic representation (NEAR). To enhance the predictions with small data sets, we construct multi-task (MT) AweGNN models in addition to single-task (ST) AweGNN models. The proposed methods are applied to various benchmark data sets, including four data sets for quantitative toxicity analysis and another data set for solvation prediction. Extensive numerical tests show that AweGNN models can achieve state-of-the-art performance in molecular property predictions.

CATMoS: Collaborative Acute Toxicity Modeling Suite

BACKGROUND

Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals.

OBJECTIVES

The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg).

METHODS

An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches.

RESULTS

The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results.

DISCUSSION

CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Head, Shoulders, Knees, and Toes: Placement of Wearable Passive Samplers Alters Exposure Profiles Observed

Chemical exposures are a major risk factor for many diseases. Comprehensive characterization of personal exposures is necessary to highlight chemicals of concern and factors that influence these chemical exposure dynamics. For this purpose, wearable passive samplers can be applied to assess longitudinal personal exposures to airborne contaminants. Questions remain regarding the impact of sampler placement at different locations of the body on the exposure profiles observed and how these placements affect the monitoring of seasonal dynamics in exposures. This study assessed personal air contaminant exposure using passive samplers worn in parallel across 32 participant's wrists, chest, and shoes over 24 h. Samplers were analyzed by thermal desorption gas chromatography high-resolution mass spectrometry. Personal exposure profiles were similar for about one-third of the 275 identified chemicals, irrespective of sampler placement. Signals of certain semivolatile organic compounds (SVOCs) were enhanced in shoes and, to a lesser extent, wrist samplers, as compared to those in chest samplers. Signals of volatile organic compounds were less impacted by sampler placement. Results showed that chest samplers predominantly captured more volatile exposures, as compared to those of particle-bound exposures, which may indicate predominant monitoring of chemicals via the inhalation route of exposure for chest samplers. In contrast, shoe samplers were more sensitive to particle-bound SVOCs. Seventy-one chemicals changed across participants between winter and summer in the same manner for two or more different sampler placements on the body, whereas 122 chemicals were observed to have seasonal differences in only one placement. Hence, the placement in certain cases significantly impacts exposure dynamics observed. This work shows that it is essential in epidemiological studies undertaking exposure assessment to consider the consequence of the placement of exposure monitors.

Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein

X-linked inhibitor of apoptosis protein (XIAP) is a member of inhibitor of apoptosis protein (IAP) family responsible for neutralizing the caspases-3, caspases-7, and caspases-9. Overexpression of the protein decreased the apoptosis process in the cell and resulting development of cancer. Different types of XIAP antagonists are generally used to repair the defective apoptosis process that can eliminate carcinoma from living bodies. The chemically synthesis compounds discovered till now as XIAP inhibitors exhibiting side effects, which is making difficulties during the treatment of chemotherapy. So, the study has design to identifying new natural compounds that are able to induce apoptosis by freeing up caspases and will be low toxic. To identify natural compound, a structure-based pharmacophore model to the protein active site cavity was generated following by virtual screening, molecular docking and molecular dynamics (MD) simulation. Initially, seven hit compounds were retrieved and based on molecular docking approach four compounds has chosen for further evaluation. To confirm stability of the selected drug candidate to the target protein the MD simulation approach were employed, which confirmed stability of the three compounds. Based on the finding, three newly obtained compounds namely Caucasicoside A (ZINC77257307), Polygalaxanthone III (ZINC247950187), and MCULE-9896837409 (ZINC107434573) may serve as lead compounds to fight against the treatment of XIAP related cancer, although further evaluation through wet lab is necessary to measure the efficacy of the compounds.

Exploring the external exposome using wearable passive samplers - The China BAPE study

Environmental exposures are one of the greatest threats to human health, yet we lack tools to answer simple questions about our exposures: what are our personal exposure profiles and how do they change overtime (external exposome), how toxic are these chemicals, and what are the sources of these exposures? To capture variation in personal exposures to airborne chemicals in the gas and particulate phases and identify exposures which pose the greatest health risk, wearable exposure monitors can be deployed. In this study, we deployed passive air sampler wristbands with 84 healthy participants (aged 60-69 years) as part of the Biomarkers for Air Pollutants Exposure (China BAPE) study. Participants wore the wristband samplers for 3 days each month for five consecutive months. Passive samplers were analyzed using a novel gas chromatography high resolution mass spectrometry data-processing workflow to overcome the bottleneck of processing large datasets and improve confidence in the resulting identified features. The toxicity of chemicals observed frequently in personal exposures were predicted to identify exposures of potential concern via inhalation route or other routes of airborne contaminant exposure. Three exposures were highlighted based on elevated toxicity: dichlorvos from insecticides (mosquito/malaria control), naphthalene partly from mothballs, and 183 polyaromatic hydrocarbons from multiple sources. Other exposures explored in this study are linked to diet and personal care products, cigarette smoke, sunscreen, and antimicrobial soaps. We highlight the potential for this workflow employing wearable passive samplers for prioritizing chemicals of concern at both the community and individual level, and characterizing sources of exposures for follow up interventions.

Machine Learning for Precision Breast Cancer Diagnosis and Prediction of the Nanoparticle Cellular Internalization

In the field of theranostics, diagnostic nanoparticles are designed to collect highly patient-selective disease profiles, which is then leveraged by a set of nanotherapeutics to improve the therapeutic results. Despite their early promise, high interpatient and intratumoral heterogeneities make any rational design and analysis of these theranostics platforms extremely problematic. Recent advances in deep-learning-based tools may help bridge this gap, using pattern recognition algorithms for better diagnostic precision and therapeutic outcome. Triple-negative breast cancer (TNBC) is a conundrum because of the complex molecular diversity, making its diagnosis and therapy challenging. To address these challenges, we propose a method to predict the cellular internalization of nanoparticles (NPs) against different cancer stages using artificial intelligence. Here, we demonstrate for the first time that a combination of machine-learning (ML) algorithm and characteristic cellular uptake responses for individual cancer cell types can be successfully used to classify various cancer cell types. Utilizing this approach, we can optimize the nanomaterials to get an optimum structure-internalization response for a given particle. This methodology predicted the structure-internalization response of the evaluated nanoparticles with remarkable accuracy (Q² = 0.9). We anticipate that it can reduce the effort by minimizing the number of nanoparticles that need to be tested and could be utilized as a screening tool for designing nanotherapeutics. Following this, we have proposed a diagnostic nanomaterial-based platform used to assemble a patient-specific cancer profile with the assistance of machine learning (ML). The platform is composed of eight carbon nanoparticles (CNPs) with multifarious surface chemistries that can differentiate healthy breast cells from cancerous cells and then subclassify TNBC cells vs non-TNBC cells, within the TNBC group. The artificial neural network (ANN) algorithm has been successfully used in identifying the type of cancer cells from 36 unknown cancer samples with an overall accuracy of >98%, providing potential applications in cancer diagnostics.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

BACKGROUND

Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling.

OBJECTIVES

In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP).

METHODS

The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays.

RESULTS

The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set.

DISCUSSION

The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼ 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

The software tool to find greener solvent replacements, PARIS III

PARIS III (Program for Assisting the Replacement of Industrial Solvents III, Version 1.4.0) is a pollution prevention solvent substitution software tool used to find mixtures of solvents that are less harmful to the environment than the industrial solvents to be replaced. By searching extensively though hundreds of millions of possible solvent combinations, mixtures that perform the same as the original solvents may be found. Greener solvent substitutes may then be chosen from those mixtures that behave similarly but have less environmental impact. These extensive searches may be enhanced by fine-tuning impact weighting factors to better reflect regional environmental concerns; and by adjusting how close the properties of the replacement must be to those of the original solvent. Optimal replacements can then be compared again and selected for better performance, but less environmental impact. This method can be a very effective way of finding greener replacements for harmful solvents used by industry.

Screening for Improved Nerve Agent Simulants and Insights into Organophosphate Hydrolysis Reactions from DFT and QSAR Modeling

The effective detoxification of chemical warfare agents, specifically nerve agents, is a pressing issue in the modern world. Due to the high toxicity of these molecules, simulants are often used in experiments as substitutes for the agents. However, there is little reason to believe that the current simulants used in the literature are optimal predictors of nerve agent reactivity. Density functional theory calculations were performed on the alkaline hydrolysis of over 100 organophosphate molecules to identify improved simulants for the G-series nerve agents soman and sarin, based on low toxicity and similarity to nerve agent hydrolysis energetics and degradation mechanism. This screening highlighted 5 molecules that have nearly identical reaction barriers to the actual agents, while being far less toxic. Quantitative structure-activity relationship (QSAR) models were also derived to determine the most significant molecular descriptors for describing the hydrolysis free energy barriers of these reactions. The optimal QSAR model was subjected to a thorough statistical analysis and validation procedure to confirm its predictive capacity, showing excellent quantitative and ranking accuracy. It was further shown that the model trained on G-series agents can reliably predict energetics for other organophosphate classes as well, including VX. Through these computational insights, experimentalists may be aided in accurately and safely studying these reactions with less toxic simulants.

Framework towards more Sustainable Chemical Synthesis Design - A Case Study of Organophosphates

In recent years, the advancement of sustainable chemistry concepts and approaches along with their demonstrated application has become a central part of the design, synthesis, and manufacture of a chemical. Sustainable chemistry not only utilizes the principles of green chemistry, but also expands to incorporate economic, societal, and environmental aspects. This is further elucidated by the incorporation of life cycle assessment/thinking to include the raw material production, manufacture, processing, and use and disposal stages, allowing for a comprehensive evaluation of the environmental and human health impacts attributed to a chemical. This contribution outlines an approach for the development of a preliminary framework for the sustainable synthesis of a chemical that is identified as an alternative for an existing chemical of concern. The framework is introduced concurrently with a case study for organophosphates that are selected as potential replacements for brominated flame retardants (BFRs). This framework is designed to apply existing knowledge of green chemistry to the synthesis of alternatives, along with its integration into Life Cycle Assessment culminating in the development of a more overall sustainable chemical entity when compared to its predecessor.

Demonstration of a consensus approach for the calculation of physicochemical properties required for environmental fate assessments

Eight software applications are compared for their performance in estimating the octanol-water partition coefficient (Kow), melting point, vapor pressure and water solubility for a dataset of polychlorinated biphenyls, polybrominated diphenyl ethers, polychlorinated dibenzodioxins, and polycyclic aromatic hydrocarbons. The predicted property values are compared against a curated dataset of measured property values compiled from the scientific literature with careful consideration given to the analytical methods used for property measurements of these hydrophobic chemicals. The variability in the predicted values from different calculators generally increases for higher values of Kow and melting point and for lower values of water solubility and vapor pressure. For each property, no individual calculator outperforms the others for all four of the chemical classes included in the analysis. Because calculator performance varies based on chemical class and property value, the geometric mean and the median of the calculated values from multiple calculators that use different estimation algorithms are recommended as more reliable estimates of the property value than the value from any single calculator.

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

Previous modelling of the median lethal dose (oral rat LD₅₀) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r² = 0.50) than the LDA method (r² ~ 0.47). A single model fit to the entire training set yielded the poorest results (r² = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.

A framework for an alternatives assessment dashboard for evaluating chemical alternatives applied to flame retardants for electronic applications

The goal of alternatives assessment (AA) is to facilitate a comparison of alternatives to a chemical of concern, resulting in the identification of safer alternatives. A two stage methodology for comparing chemical alternatives was developed. In the first stage, alternatives are compared using a variety of human health effects, ecotoxicity, and physicochemical properties. Hazard profiles are completed using a variety of online sources and quantitative structure activity relationship models. In the second stage, alternatives are evaluated utilizing an exposure/risk assessment over the entire life cycle. Exposure values are calculated using screening-level near-field and far-field exposure models. The second stage allows one to more accurately compare potential exposure to each alternative and consider additional factors that may not be obvious from separate binned persistence, bioaccumulation, and toxicity scores. The methodology was utilized to compare phosphate-based alternatives for decabromodiphenyl ether (decaBDE) in electronics applications.